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Source code for datasets.sarcos

# Copyright 2011 Hugo Larochelle. All rights reserved.# # Redistribution and use in source and binary forms, with or without modification, are# permitted provided that the following conditions are met:# # 1. Redistributions of source code must retain the above copyright notice, this list of# conditions and the following disclaimer.# # 2. Redistributions in binary form must reproduce the above copyright notice, this list# of conditions and the following disclaimer in the documentation and/or other materials# provided with the distribution.# # THIS SOFTWARE IS PROVIDED BY Hugo Larochelle ``AS IS'' AND ANY EXPRESS OR IMPLIED# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND# FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL Hugo Larochelle OR# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.# # The views and conclusions contained in the software and documentation are those of the# authors and should not be interpreted as representing official policies, either expressed# or implied, of Hugo Larochelle."""Module ``datasets.sarcos`` gives access to the SARCOS dataset.This is a multi-dimensional regression dataset, with outputs in [0,1].The task is an inverse dynamics problem for a seven degrees-of-freedomSARCOS anthropomorphic robot arm.The inputs have varying range, so PCA is recommended.| **References:**| LWPR: An O(n) Algorithm for Incremental Real Time Learning in High Dimensional Space| Vijayakumar and Schaal| http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.63.4252&rep=rep1&type=pdf|| The Gaussian Processes Web Site| http://www.gaussianprocess.org/gpml/data/"""importmlpython.misc.ioasmlioimportnumpyasnpimportos

[docs]defload(dir_path,load_to_memory=False):""" SARCOS inverse dynamics dataset. The data is given by a dictionary mapping from strings ``'train'``, ``'valid'`` and ``'test'`` to the associated pair of data and metadata. **Defined metadata:** * ``'input_size'`` * ``'target_size'`` * ``'length'`` """input_size=21target_size=7dir_path=os.path.expanduser(dir_path)defload_line(line):tokens=line.split()return(np.array([float(i)foriintokens[:input_size]]),np.array([float(i)foriintokens[input_size:]]))train_file,valid_file,test_file=[os.path.join(dir_path,'sarcos_'+ds+'.txt')fordsin['train','valid','test']]# Get datatrain,valid,test=[mlio.load_from_file(f,load_line)forfin[train_file,valid_file,test_file]]lengths=[40036,4448,4449]ifload_to_memory:train,valid,test=[mlio.MemoryDataset(d,[(input_size,),(target_size,)],[np.float64,np.float64],l)ford,linzip([train,valid,test],lengths)]# Get metadatatrain_meta,valid_meta,test_meta=[{'input_size':input_size,'target_size':target_size,'length':l}forlinlengths]return{'train':(train,train_meta),'valid':(valid,valid_meta),'test':(test,test_meta)}

[docs]defobtain(dir_path):""" Downloads the dataset to ``dir_path``. """dir_path=os.path.expanduser(dir_path)print'Downloading the dataset'importurlliburllib.urlretrieve('http://www.gaussianprocess.org/gpml/data/sarcos_inv.mat',os.path.join(dir_path,'sarcos_inv.mat'))urllib.urlretrieve('http://www.gaussianprocess.org/gpml/data/sarcos_inv_test.mat',os.path.join(dir_path,'sarcos_inv_test.mat'))# Writing everything into text files, to allow for not loading the data into memorydefwrite_to_txt_file(mat,filename):f=open(filename,'w')format_iinmat:line=' '.join(['%.6f'%mat_ijformat_ijinmat_i])+'\n'f.write(line)f.close()importscipy.iotrain_valid_set=scipy.io.loadmat(os.path.join(dir_path,'sarcos_inv.mat'))['sarcos_inv']valid_size=round(0.1*len(train_valid_set))train_size=len(train_valid_set)-valid_sizeimportrandomrandom.seed(12345)perm=range(len(train_valid_set))random.shuffle(perm)train_valid_set=train_valid_set[perm,:]train_set=train_valid_set[:train_size,:]valid_set=train_valid_set[train_size:,:]test_set=scipy.io.loadmat(os.path.join(dir_path,'sarcos_inv_test.mat'))['sarcos_inv_test']write_to_txt_file(train_set,os.path.join(dir_path,'sarcos_train.txt'))write_to_txt_file(valid_set,os.path.join(dir_path,'sarcos_valid.txt'))write_to_txt_file(test_set,os.path.join(dir_path,'sarcos_test.txt'))print'Done '